Finally, besides the characteristics and size of the datasets, the similarity of donor/acceptor properties remains an important question to determine a possibility of applying the transfer learning approach. We trained three groups of models: ‘pure’ GCNN models, without the use of transfer learning; the same models, transferred from the respective donor models; and models using a widespread combination of random forest algorithm [1]}, [2]} and ECFP6 fingerprints [3]} for molecules feature extraction as a baseline. In Fig. 5 the regression and classification metrics comparison for datasets with best quality models among all splitting properties are presented.
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